Academic Integrity in the Age of AI: Ethical Risks, Hallucinatory Citations, and Collaborative Governance

Abstract

The deep integration of generative artificial intelligence into the entire research workfl ow has triggered an unprecedented paradigm crisis in the fi eld of academic ethics. This paper systematically categorizes the primary types of ethical misconduct in AI applications, including data ethics risks, algorithmic bias infi ltration, ambiguity regarding authorship, intellectual property disputes, and the erosion of research integrity. The paper proposes a four-dimensional approach to identifying these issues: fostering ethical sensitivity, auditing technological transparency, documenting processes, and innovating peer review. It also constructs a four-tiered governance strategy encompassing author self-regulation, institutional restructuring, ethical design technologies, and global collaborative governance. The study argues that resolving the academic ethics crisis in the AI era requires a shift from post-facto punishment to process-embedded oversight, from individual self-discipline to systemic safeguards, and from a human-centered approach to human-machine symbiosis, thereby upholding and reinforcing the core values of academic ethics.